I am trying to estimate a nonlinear model in Dynare with a mix of filtered and unfiltered data. I read Johannes’ excellent guide of specifying observation equations, and I just wanted to clarify that I’ve specified the equations correctly.
I have the following data: R_{t}^{obs} : gross nominal interest rate (quarterly) (unfiltered) \Pi_{t}^{obs} : gross nominal inflation rate (quarterly) (unfiltered) e_{t}^{obs} : log differences of the real exchange rate (demeaned) r_{t}^{k,obs} : net rate of return on capital (quarterly) (unfiltered)
Currently, in my Dynare code I have the following observation equations:
Where R, \Pi, and e are entered in the other model equations using exp() transformations, and where rk is left as-is.
I am a bit unsure if I have specified the return on capital observation equation correctly. The way it is now, I would have to use Dynare to estimate Rk_bar, as rk is basically – using common notation – “rk_hat”.
Why are you matching an empirical log difference of the exchange rate to a percentage deviation from steady state?
If you use an exp()-transformation, then your variables will be in logs. This will turn a gross rate into a net one. So you are matching net rates in the model to gross rates in the data.
You did not explicitly say what rk is in the model. If it’s a log deviation of the gross return on capital from steady state, then adding back the log steady state will result in the net return on capital as observed in the data.
where lnR_obs and lnPi_obs is the logged nominal gross interest rate and inflation in the data, respectively. rk, is not in logs in the model and is simply the net return/rental rate of capital.
As for e_{t}^{obs}, since the data is log differences of the real exchange rate, then I would have to specify the measurement equation as e-e(-1). However, I’m not sure if this is doable as I couldn’t find an example in your paper. Have I assumed the timing convention correctly?
I’m now in the process of estimating the model but I’m getting a “posterior kernel optimisation problem”. I’m not quite sure how to proceed. Would you mind taking a look at the mod file?
The condition for the Hessian is for an interior maximum. It does not hold at corners. You can see in the mode_file that the entries related to zeta are NaN.
Hi, I post here because I don’t think a new thread is deserved.
I have to perform nonlinear estimation. In such a case should I demean my data on the inflation rate as to be consistent with the steady state of the model (0 inflation) or the ergodic mean of the model (which I find to be non-negligibly negative)?
Has anyone ever treated the data to be consistent with the ergodic mean?
I would like to first estimate the model using the same dataset by Smets and Wouters (2007), and I got somewhat confused about matching the model-implied to the observed variables, and the scaling-by-100 issue. I was wondering if, after dividing their observables by 100, the following measurement equations are correct:
dy = Y - Y(-1) + g_c;
dc = C - C(-1) + g_c;
dinve = I - I(-1) + g_c;
dw = W - W(-1) + g_c;
pinfobs = pi_c;
robs = i;
labobs = L;
where Y, C, I, and W are detrended variables, and g_c is the economy’s growth rate. All variables enter the model equilibrium conditions with the exp() transformation.
I have also taken the same priors as Smets and Wouters (2007) and rescaled the prior of the shocks’ standard deviations by dividing them by 100. Is this correct?
How should I instead proceed if I wanted to use the original dataset and priors (w/o dividing by 100)? Could I simply multiply the endogenous variables (except for g_c) by 100 in the observation equations? Or am I completely off-track?